For release 10:00 a.m. (EDT), Wednesday, July 7, 2010 USDL-10-0932
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
County Employment and Wages
Fourth Quarter 2009
From December 2008 to December 2009, employment declined in 325 of
the 334 largest U.S. counties according to preliminary data, the U.S.
Bureau of Labor Statistics reported today. Trumbull, Ohio, posted the
largest percentage decline, with a loss of 8.6 percent over the year,
compared with a national job decrease of 4.1 percent. Almost 54
percent of the employment decline in Trumbull occurred in
manufacturing, which lost 3,504 jobs over the year (-22.7 percent).
Arlington, Va., experienced the largest over-the-year percentage
increase in employment among the largest counties in the U.S., with a
gain of 0.5 percent.
The U.S. average weekly wage increased by 2.5 percent over the year.
Among the large counties in the U.S., Douglas, Colo., had the largest
over-the-year increase in average weekly wages in the fourth quarter
of 2009, with a gain of 26.1 percent. Within Douglas, professional
and business services had the largest over-the-year increase in
average weekly wages with a gain of 99.8 percent. A fourth-quarter
acquisition in this industry resulted in large payouts, which may
include bonuses, severance pay, and stock options. St. Louis City,
Mo., experienced the largest decline in average weekly wages with a
loss of 33.9 percent over the year. This decline reflects a return
from very high levels in 2008 caused by an acquisition in
professional and business services and manufacturing.
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| |
| A redesign of the County Employment and Wages news release will be |
| implemented with the first quarter 2010 release. Table 3, along with |
| the associated text on the largest county by state, will be removed. |
| |
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Table A. Top 10 large counties ranked by December 2009 employment, December 2008-09 employment
decrease, and December 2008-09 percent decrease in employment
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Employment in large counties
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December 2009 employment | Decrease in employment, | Percent decrease in employment,
(thousands) | December 2008-09 | December 2008-09
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 128,334.9| United States -5,521.5| United States -4.1
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| |
Los Angeles, Calif. 3,926.0| Los Angeles, Calif. -217.9| Trumbull, Ohio -8.6
Cook, Ill. 2,369.9| Maricopa, Ariz. -113.0| Oakland, Mich. -8.1
New York, N.Y. 2,294.4| Cook, Ill. -111.1| Peoria, Ill. -8.0
Harris, Texas 1,990.2| New York, N.Y. -93.6| Seminole, Fla. -7.9
Maricopa, Ariz. 1,626.8| Harris, Texas -90.0| Sedgwick, Kan. -7.7
Dallas, Texas 1,409.9| Orange, Calif. -89.7| Tulare, Calif. -7.6
Orange, Calif. 1,361.4| San Diego, Calif. -64.6| Winnebago, Ill. -7.6
San Diego, Calif. 1,245.3| Dallas, Texas -63.6| Catawba, N.C. -7.5
King, Wash. 1,119.1| Clark, Nev. -60.7| Kern, Calif. -7.4
Miami-Dade, Fla. 959.7| Santa Clara, Calif. -56.8| Macomb, Mich. -7.3
| |
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Of the 334 largest counties in the United States (as measured by 2008
annual average employment), 159 had over-the-year percentage declines
in employment greater than or equal to the national average (-4.1
percent) in December 2009, 166 large counties experienced smaller
declines than the national average, and 3 counties experienced
employment gains. The percent change in average weekly wages was
equal to or greater than the national average (2.5 percent) in 196 of
the largest U.S. counties and was below the national average in 133
counties.
The employment and average weekly wage data by county are compiled
under the Quarterly Census of Employment and Wages (QCEW) program,
also known as the ES-202 program. The data are derived from reports
submitted by every employer subject to unemployment insurance (UI)
laws. The 9.1 million employer reports cover 128.3 million full- and
part-time workers.
Large County Employment
In December 2009, national employment, as measured by the QCEW
program, was 128.3 million, down by 4.1 percent from December 2008.
The 334 U.S. counties with 75,000 or more employees accounted for
71.4 percent of total U.S. employment and 77.1 percent of total
wages. These 334 counties had a net job decline of 4,119,900 over the
year, accounting for 74.6 percent of the overall U.S. employment
decrease.
Employment declined in 325 counties from December 2008 to December
2009. The largest percentage decline in employment was in Trumbull,
Ohio (-8.6 percent). Oakland, Mich., had the next largest percentage
decline (-8.1 percent), followed by the counties of Peoria, Ill.
(-8.0 percent), Seminole, Fla. (-7.9 percent), and Sedgwick, Kan. (-7.7
percent). The largest decline in employment levels occurred in Los
Angeles, Calif. (-217,900), followed by the counties of Maricopa,
Ariz. (-113,000), Cook, Ill. (-111,100), New York, N.Y. (-93,600),
and Harris, Texas (-90,000). (See table A.) Combined employment
losses in these five counties over the year totaled 625,600, or 11.3
percent of the employment decline for the U.S. as a whole.
Table B. Top 10 large counties ranked by fourth quarter 2009 average weekly wages, fourth quarter 2008-09
increase in average weekly wages, and fourth quarter 2008-09 percent increase in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Increase in average weekly | Percent increase in average
fourth quarter 2009 | wage, fourth quarter 2008-09 | weekly wage, fourth
| | quarter 2008-09
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| |
United States $942| United States $23| United States 2.5
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| |
New York, N.Y. $1,878| Douglas, Colo. $244| Douglas, Colo. 26.1
Santa Clara, Calif. 1,699| Santa Clara, Calif. 129| Alachua, Fla. 10.1
Washington, D.C. 1,614| Durham, N.C. 108| Durham, N.C. 9.5
Fairfield, Conn. 1,607| Arlington, Va. 87| Elkhart, Ind. 8.6
Arlington, Va. 1,594| Montgomery, Md. 76| Santa Clara, Calif. 8.2
Suffolk, Mass. 1,565| Alachua, Fla. 74| Montgomery, Ala. 8.0
San Francisco, Calif. 1,539| Fairfax, Va. 73| McLean, Ill. 7.9
Fairfax, Va. 1,489| Montgomery, Ala. 66| Okaloosa, Fla. 7.5
San Mateo, Calif. 1,477| McLean, Ill. 66| McLennan, Texas 7.4
Morris, N.J. 1,429| Morris, N.J. 64| Lucas, Ohio 7.0
| Montgomery, Pa. 64|
| |
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Employment rose in three of the large counties from December 2008 to
December 2009. Arlington, Va., had the largest over-the-year
percentage increase in employment (0.5 percent), followed by Bronx,
N.Y., and Kings, N.Y. (0.2 percent each).
Large County Average Weekly Wages
Average weekly wages for the nation increased by 2.5 percent over the
year ending in the fourth quarter of 2009. Among the 334 largest
counties, 305 had over-the-year increases in average weekly wages in
the fourth quarter. The largest wage gain occurred in Douglas, Colo.,
with an increase of 26.1 percent from the fourth quarter of 2008.
Alachua, Fla., had the second largest gain (10.1 percent), followed
by the counties of Durham, N.C. (9.5 percent), Elkhart, Ind. (8.6
percent), and Santa Clara, Calif. (8.2 percent). (See table B.)
Of the 334 largest counties, 23 experienced declines in average
weekly wages. St. Louis City, Mo., led the nation in average weekly
wage decline with a loss of 33.9 percent over the year. Within St.
Louis City, large payouts related to an acquisition were distributed
within professional and business services and manufacturing
industries in the fourth quarter of 2008. Manufacturing had the
largest over-the-year decline in average weekly wages (-57.9 percent)
followed by professional and business services (-56.2 percent).
Somerset, N.J., had the second largest overall decline (-6.2
percent), followed by the counties of Clayton, Ga. (-5.3 percent),
Calcasieu, La. (-5.1 percent), and Lake, Ind. (-3.4 percent).
The national average weekly wage in the fourth quarter of 2009 was
$942. Average weekly wages were higher than the national average in
105 of the 334 largest U.S. counties. New York, N.Y., held the top
position among the highest-paid large counties with an average weekly
wage of $1,878. Santa Clara, Calif., was second with an average
weekly wage of $1,699, followed by Washington, D.C. ($1,614),
Fairfield, Conn. ($1,607), and Arlington, Va. ($1,594). There were
226 counties with an average weekly wage below the national average
in the fourth quarter of 2009. The lowest average weekly wage was
reported in Horry, S.C. ($584), followed by the counties of Cameron,
Texas, Hidalgo, Texas ($598 each), Webb, Texas ($619), and Yakima,
Wash. ($640). (See table 1.)
Average weekly wages are affected not only by changes in total wages
but also by employment changes in high- and low-paying industries.
(See Technical Note.) The 2.5-percent over-the-year increase in
average weekly wages for the nation was partially due to large
employment declines in low-paying industries such as trade,
transportation, and utilities. (See table 2.)
Ten Largest U.S. Counties
All of the 10 largest counties (based on 2008 annual average
employment levels) experienced over-the-year percent declines in
employment in December 2009. Maricopa, Ariz., experienced the largest
decline in employment among the 10 largest counties with a 6.5
percent decrease. Within Maricopa, every private industry group
except education and health services experienced an employment
decline, with construction experiencing the largest decline (-28.5
percent). (See table 2.) Orange, Calif., had the next largest decline
in employment (-6.2 percent), followed by Los Angeles, Calif. (-5.3
percent). New York, N.Y., experienced the smallest decline in
employment (-3.9 percent) among the 10 largest counties. Dallas,
Texas, and Harris, Texas, had the second smallest employment losses
(-4.3 percent each).
All of the 10 largest U.S. counties saw an over-the-year increase in
average weekly wages. San Diego, Claif., experienced the largest
increase in average weekly wages among the 10 largest counties with a
gain of 3.7 percent. This average weekly wage growth was a result of
a large employment loss in the professional and business services
supersector. Employment dropped by 7.2 percent while total wages only
dropped by 2.7 percent, thus average weekly wages for this
supersector increased by 4.8 percent. San Diego’s average weekly wage
growth was followed by King, Wash. (3.6 percent), and Maricopa, Ariz.
(3.4 percent).
Largest County by State
Table 3 shows December 2009 employment and the 2009 fourth quarter
average weekly wage in the largest county in each state, which is
based on 2008 annual average employment levels. The employment levels
in the counties ranged from 3.9 million in Los Angeles, Calif., to
42,600 in Laramie, Wyo. The highest average weekly wage of these
counties was in New York, N.Y. ($1,878), while the lowest average
weekly wage was in Yellowstone, Mont. ($768).
For More Information
The tables included in this release contain data for the nation and
for the 334 U.S. counties with annual average employment levels of
75,000 or more in 2008. December 2009 employment and 2009 fourth
quarter average weekly wages for all states are provided in table 4
of this release.
For additional information about the quarterly employment and wages
data, please read the Technical Note. Data for the fourth quarter of
2009 will be available at http://www.bls.gov/cew/. Additional
information about the QCEW data may be obtained by calling (202) 691-
6567.
Several BLS regional offices are issuing QCEW news releases targeted
to local data users. For links to these releases, see
http://www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for first quarter 2010 is
scheduled to be released on Tuesday, October 19, 2010.
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| |
| The QCEW State and County Map application was released on June 30, |
| 2010 (http://beta.bls.gov/maps). This new feature of the BLS |
| website provides users with supersector industry employment and |
| wages at the national, state, and county levels. Data are presented |
| in map, tabular, and downloadable formats. |
| |
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Technical Note
These data are the product of a federal-state cooperative program, the Quarterly
Census of Employment and Wages (QCEW) program, also known as the ES-202 program.
The data are derived from summaries of employment and total pay of workers covered
by state and federal unemployment insurance (UI) legislation and provided by State
Workforce Agencies (SWAs). The summaries are a result of the administration of
state unemployment insurance programs that require most employers to pay quarterly
taxes based on the employment and wages of workers covered by UI. QCEW data in this
release are based on the 2007 North American Industry Classification System. Data
for 2009 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment le-
vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro-
vided, but not used in calculating U.S. averages, rankings, or in the analysis in
the text. Each year, these large counties are selected on the basis of the prelimi-
nary annual average of employment for the previous year. The 335 counties presented
in this release were derived using 2008 preliminary annual averages of employment.
For 2009 data, two counties have been added to the publication tables: Johnson,
Iowa, and Gregg, Texas. These counties will be included in all 2009 quarterly re-
leases. Two counties, Boone, Ky., and St. Tammany, La., which were published in the
2008 releases, will be excluded from this and future 2009 releases because their
2008 annual average employment levels were less than 75,000. The counties in table
2 are selected and sorted each year based on the annual average employment from the
preceding year.
The preliminary QCEW data presented in this release may differ from data released
by the individual states. These potential differences result from the states' con-
tinuing receipt of UI data over time and ongoing review and editing. The individual
states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for
any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED),
and Current Employment Statistics (CES)--makes use of the quarterly UI employment
reports in producing data; however, each measure has a somewhat different universe
coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different
measures of employment change over time. It is important to understand program dif-
ferences and the intended uses of the program products. (See table.) Additional in-
formation on each program can be obtained from the program Web sites shown in the
table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
---------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 400,000 establish-
| submitted by 9.1 | ministrative records| ments
| million establish- | submitted by 6.8 |
| ments in first | million private-sec-|
| quarter of 2009 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -7 months after the| -8 months after the | -Usually first Friday
| end of each quar- | end of each quarter| of following month
| ter | |
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and annu-
| new quarter of UI | dinal database and | ally realigns (bench-
| data | directly summarizes | marks) sample esti-
| | gross job gains and | mates to first quar-
| | losses | ter UI levels
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from
quarterly contribution reports submitted to the SWAs by employers. For federal ci-
vilian workers covered by the Unemployment Compensation for Federal Employees
(UCFE) program, employment and wage data are compiled from quarterly reports sub-
mitted by four major federal payroll processing centers on behalf of all federal
agencies, with the exception of a few agencies which still report directly to the
individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the
"Multiple Worksite Report," which provides detailed information on the location and
industry of each of their establishments. QCEW employment and wage data are derived
from microdata summaries of 9.1 million employer reports of employment and wages
submitted by states to the BLS in 2008. These reports are based on place of employ-
ment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state
since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef-
fective, expanding coverage to include most State and local government employees.
In 2008, UI and UCFE programs covered workers in 134.8 million jobs. The estimated
129.4 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.5 percent of civilian wage and salary employment. Covered workers
received $6.142 trillion in pay, representing 93.8 percent of the wage and salary
component of personal income and 42.5 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural
workers on small farms, all members of the Armed Forces, elected officials in most
states, most employees of railroads, some domestic workers, most student workers at
schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on
the employment and wages reported by employers covered under the UI program. Cover-
age changes may affect the over-the-year comparisons presented in this news re-
lease.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received
pay for the pay period including the 12th of the month. With few exceptions, all
employees of covered firms are reported, including production and sales workers,
corporation officials, executives, supervisory personnel, and clerical workers.
Workers on paid vacations and part-time workers also are included.
Average weekly wage values are calculated by dividing quarterly total wages by the
average of the three monthly employment levels (all employees, as described above)
and dividing the result by 13, for the 13 weeks in the quarter. These calculations
are made using unrounded employment and wage values. The average wage values that
can be calculated using rounded data from the BLS database may differ from the av-
erages reported. Included in the quarterly wage data are non-wage cash payments
such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compen-
sation plans such as 401(k) plans and stock options. Over-the-year comparisons of
average weekly wages may reflect fluctuations in average monthly employment and/or
total quarterly wages between the current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as
well as the number of individuals in high-paying and low-paying occupations and the
incidence of pay periods within a quarter. For instance, the average weekly wage of
the work force could increase significantly when there is a large decline in the
number of employees that had been receiving below-average wages. Wages may include
payments to workers not present in the employment counts because they did not work
during the pay period including the 12th of the month. When comparing average week-
ly wage levels between industries, states, or quarters, these factors should be
taken into consideration.
Federal government pay levels are subject to periodic, sometimes large, fluctua-
tions due to a calendar effect that consists of some quarters having more pay pe-
riods than others. Most federal employees are paid on a biweekly pay schedule. As a
result of this schedule, in some quarters, federal wages contain payments for six
pay periods, while in other quarters their wages include payments for seven pay pe-
riods. Over-the-year comparisons of average weekly wages may reflect this calendar
effect. Higher growth in average weekly wages may be attributed, in part, to a com-
parison of quarterly wages for the current year, which include seven pay periods,
with year-ago wages that reflect only six pay periods. An opposite effect will oc-
cur when wages in the current period, which contain six pay periods, are compared
with year-ago wages that include seven pay periods. The effect on over-the-year pay
comparisons can be pronounced in federal government due to the uniform nature of
federal payroll processing. This pattern may exist in private sector pay; however,
because there are more pay period types (weekly, biweekly, semimonthly, monthly) it
is less pronounced. The effect is most visible in counties with large concentra-
tions of federal employment.
In order to ensure the highest possible quality of data, states verify with employ-
ers and update, if necessary, the industry, location, and ownership classification
of all establishments on a 4-year cycle. Changes in establishment classification
codes resulting from this process are introduced with the data reported for the
first quarter of the year. Changes resulting from improved employer reporting also
are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of indi-
vidual establishment records and reflect the number of establishments that exist in
a county or industry at a point in time. Establishments can move in or out of a
county or industry for a number of reasons--some reflecting economic events, others
reflecting administrative changes. For example, economic change would come from a
firm relocating into the county; administrative change would come from a company
correcting its county designation.
The over-the-year changes of employment and wages presented in this release have
been adjusted to account for most of the administrative corrections made to the un-
derlying establishment reports. This is done by modifying the prior-year levels
used to calculate the over-the-year changes. Percent changes are calculated using
an adjusted version of the final 2008 quarterly data as the base data. The adjusted
prior-year levels used to calculate the over-the-year percent change in employment
and wages are not published. These adjusted prior-year levels do not match the un-
adjusted data maintained on the BLS Web site. Over-the-year change calculations
based on data from the Web site, or from data published in prior BLS news releases,
may differ substantially from the over-the-year changes presented in this news re-
lease.
The adjusted data used to calculate the over-the-year change measures presented in
this release account for most of the administrative changes--those occurring when
employers update the industry, location, and ownership information of their estab-
lishments. The most common adjustments for administrative change are the result of
updated information about the county location of individual establishments. In-
cluded in these adjustments are administrative changes involving the classification
of establishments that were previously reported in the unknown or statewide county
or unknown industry categories. Beginning with the first quarter of 2008, adjusted
data account for administrative changes caused by multi-unit employers who start
reporting for each individual establishment rather than as a single entity.
The adjusted data used to calculate the over-the-year change measures presented in
any County Employment and Wages news release are valid for comparisons between the
starting and ending points (a 12-month period) used in that particular release.
Comparisons may not be valid for any time period other than the one featured in a
release even if the changes were calculated using adjusted data.
County definitions are assigned according to Federal Information Processing Stan-
dards Publications (FIPS PUBS) as issued by the National Institute of Standards and
Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of
the Information Technology Management Reform Act of 1996 and the Computer Security
Act of 1987, Public Law 104-106. Areas shown as counties include those designated
as independent cities in some jurisdictions and, in Alaska, those designated as
census areas where counties have not been created. County data also are presented
for the New England states for comparative purposes even though townships are the
more common designation used in New England (and New Jersey). The regions referred
to in this release are defined as census regions.
Additional statistics and other information
An annual bulletin, Employment and Wages, features comprehensive information by de-
tailed industry on establishments, employment, and wages for the nation and all
states. The 2008 edition of this bulletin contains selected data produced by Busi-
ness Employment Dynamics (BED) on job gains and losses, as well as selected data
from the first quarter 2009 version of this news release. Tables and additional
content from the 2008 Employment and Wages Annual Bulletin are now available online
at http://www.bls.gov/cew/cewbultn08.htm. These tables present final 2008 annual
averages. The tables are included on the CD which accompanies the hardcopy version
of the Annual Bulletin. Employment and Wages Annual Averages, 2008 is available
for sale as a chartbook from the United States Government Printing Office, Superin-
tendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250, telephone (866) 512-
1800, outside Washington, D.C. Within Washington, D.C., the telephone number is
(202) 512-1800. The fax number is (202) 512-2104.
News releases on quarterly measures of gross job flows also are available upon re-
quest from the Division of Administrative Statistics and Labor Turnover (Business
Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail:
BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals
upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-
800-877-8339.
Table 1. Covered(1) establishments, employment, and wages in the 335 largest counties,
fourth quarter 2009(2)
Employment Average weekly wage(4)
Establishments,
County(3) fourth quarter Percent Ranking Percent Ranking
2009 December change, by Average change, by
(thousands) 2009 December percent weekly fourth percent
(thousands) 2008-09(5) change wage quarter change
2008-09(5)
United States(6)......... 9,085.0 128,334.9 -4.1 - $942 2.5 -
Jefferson, AL............ 18.1 336.1 -5.6 263 946 2.5 193
Madison, AL.............. 8.8 179.8 -1.6 20 1,047 4.9 41
Mobile, AL............... 9.8 165.8 -5.1 241 828 2.5 193
Montgomery, AL........... 6.4 130.0 -3.7 140 891 8.0 6
Shelby, AL............... 4.8 70.7 -6.5 302 849 1.6 247
Tuscaloosa, AL........... 4.4 82.2 -4.6 209 798 1.9 230
Anchorage Borough, AK.... 8.2 147.6 -0.3 6 1,005 3.5 114
Maricopa, AZ............. 98.7 1,626.8 -6.5 302 923 3.4 119
Pima, AZ................. 20.2 350.9 -4.8 221 829 3.4 119
Benton, AR............... 5.5 90.9 -3.6 134 854 0.9 280
Pulaski, AR.............. 15.1 244.2 -2.7 64 863 1.6 247
Washington, AR........... 5.6 89.0 -2.4 50 774 3.9 87
Alameda, CA.............. 54.3 628.3 -6.6 305 1,195 2.8 167
Butte, CA................ 8.0 71.4 -3.7 140 720 3.4 119
Contra Costa, CA......... 30.0 318.4 -5.6 263 1,132 0.3 301
Fresno, CA............... 30.9 323.7 -6.5 302 759 2.8 167
Kern, CA................. 18.3 260.4 -7.4 320 820 2.0 227
Los Angeles, CA.......... 434.0 3,926.0 -5.3 248 1,099 2.0 227
Marin, CA................ 11.8 102.4 -5.6 263 1,163 1.0 276
Monterey, CA............. 12.9 141.4 -7.0 312 821 2.4 197
Orange, CA............... 102.8 1,361.4 -6.2 295 1,065 2.0 227
Placer, CA............... 10.9 122.0 -7.0 312 920 3.0 141
Riverside, CA............ 48.4 559.7 -6.4 300 757 1.7 244
Sacramento, CA........... 54.5 587.0 -4.1 170 1,019 1.3 262
San Bernardino, CA....... 50.6 606.0 -5.8 276 806 2.3 208
San Diego, CA............ 99.4 1,245.3 -4.9 225 1,019 3.7 103
San Francisco, CA........ 52.9 548.0 -4.5 201 1,539 3.1 136
San Joaquin, CA.......... 17.8 202.3 -6.0 287 816 2.4 197
San Luis Obispo, CA...... 9.7 95.7 -6.1 289 798 4.2 74
San Mateo, CA............ 24.1 324.1 -5.2 245 1,477 2.6 180
Santa Barbara, CA........ 14.5 169.5 -6.2 295 895 3.2 129
Santa Clara, CA.......... 61.6 846.5 -6.3 297 1,699 8.2 5
Santa Cruz, CA........... 9.2 86.4 -4.0 162 819 -0.2 308
Solano, CA............... 10.2 120.5 -3.8 147 921 1.9 230
Sonoma, CA............... 18.9 174.2 -6.3 297 886 -1.1 313
Stanislaus, CA........... 15.2 155.5 -6.8 310 790 4.4 61
Tulare, CA............... 9.6 140.9 -7.6 322 666 4.4 61
Ventura, CA.............. 24.0 295.5 -5.7 272 959 2.6 180
Yolo, CA................. 6.0 93.7 -6.0 287 882 -0.1 307
Adams, CO................ 9.0 147.7 -5.3 248 849 1.4 257
Arapahoe, CO............. 18.8 269.9 -3.8 147 1,094 3.8 96
Boulder, CO.............. 12.8 152.8 -3.8 147 1,069 3.2 129
Denver, CO............... 25.0 420.2 -4.7 215 1,154 3.4 119
Douglas, CO.............. 9.3 89.9 -4.8 221 1,179 26.1 1
El Paso, CO.............. 16.8 232.7 -3.7 140 863 3.6 110
Jefferson, CO............ 18.0 202.9 -4.0 162 969 4.4 61
Larimer, CO.............. 10.1 124.6 -4.2 178 841 0.5 293
Weld, CO................. 5.8 77.1 -6.6 305 772 1.3 262
Fairfield, CT............ 32.9 401.6 -4.5 201 1,607 0.7 288
Hartford, CT............. 25.4 486.0 -3.8 147 1,153 3.6 110
New Haven, CT............ 22.4 353.3 -3.6 134 1,013 3.7 103
New London, CT........... 7.0 125.7 -3.8 147 942 3.5 114
New Castle, DE........... 17.8 264.6 -5.9 282 1,070 1.9 230
Washington, DC........... 34.8 686.7 -0.1 4 1,614 2.7 173
Alachua, FL.............. 6.7 116.7 -4.1 170 810 10.1 2
Brevard, FL.............. 14.7 189.1 -4.0 162 897 4.3 67
Broward, FL.............. 63.3 689.2 -5.6 263 900 2.4 197
Collier, FL.............. 11.9 117.5 -6.6 305 832 2.7 173
Duval, FL................ 26.9 436.8 -4.5 201 911 3.9 87
Escambia, FL............. 7.9 118.9 -3.3 107 760 5.6 22
Hillsborough, FL......... 37.2 574.9 -6.1 289 927 5.8 17
Lake, FL................. 7.3 80.4 -5.3 248 674 2.6 180
Lee, FL.................. 18.8 194.9 -5.6 263 781 2.9 153
Leon, FL................. 8.2 139.1 -2.4 50 815 4.2 74
Manatee, FL.............. 9.2 111.8 -4.0 162 708 2.2 214
Marion, FL............... 8.2 90.6 (7) - 677 (7) -
Miami-Dade, FL........... 85.0 959.7 -4.5 201 949 2.9 153
Okaloosa, FL............. 6.0 75.6 -2.2 41 791 7.5 8
Orange, FL............... 35.4 648.2 -4.8 221 850 2.4 197
Palm Beach, FL........... 49.5 500.2 -5.4 253 967 5.2 33
Pasco, FL................ 9.8 96.6 -4.4 196 680 1.8 241
Pinellas, FL............. 31.0 389.2 -5.5 257 852 5.7 21
Polk, FL................. 12.5 192.8 -4.4 196 734 4.0 83
Sarasota, FL............. 14.8 134.6 -5.9 282 804 2.6 180
Seminole, FL............. 14.2 156.2 -7.9 325 791 0.8 285
Volusia, FL.............. 13.6 151.0 -4.9 225 680 2.7 173
Bibb, GA................. 4.6 79.4 -5.8 276 752 5.0 39
Chatham, GA.............. 7.7 127.9 -5.0 231 807 1.6 247
Clayton, GA.............. 4.4 107.3 -3.9 156 810 -5.3 327
Cobb, GA................. 20.7 296.4 -5.7 272 974 0.9 280
De Kalb, GA.............. 17.6 278.8 -4.2 178 971 4.7 46
Fulton, GA............... 39.3 697.4 -5.0 231 1,207 1.9 230
Gwinnett, GA............. 23.6 294.5 -5.7 272 907 1.3 262
Muscogee, GA............. 4.7 91.5 -3.9 156 757 5.1 35
Richmond, GA............. 4.8 99.3 -2.4 50 793 3.4 119
Honolulu, HI............. 25.0 435.3 -3.2 100 875 2.9 153
Ada, ID.................. 14.5 193.7 -4.9 225 824 1.5 252
Champaign, IL............ 4.2 89.3 -2.9 76 794 2.1 222
Cook, IL................. 142.6 2,369.9 -4.5 201 1,142 2.1 222
Du Page, IL.............. 36.4 548.0 -5.9 282 1,082 2.2 214
Kane, IL................. 12.9 190.3 -7.2 318 848 1.8 241
Lake, IL................. 21.4 311.4 -5.1 241 1,197 4.9 41
McHenry, IL.............. 8.6 93.5 -7.0 312 789 0.9 280
McLean, IL............... 3.7 83.7 -3.1 88 901 7.9 7
Madison, IL.............. 6.0 91.3 -4.5 201 801 4.2 74
Peoria, IL............... 4.7 97.9 -8.0 326 895 2.8 167
Rock Island, IL.......... 3.5 74.4 -6.1 289 1,115 2.9 153
St. Clair, IL............ 5.5 93.9 -3.1 88 782 3.7 103
Sangamon, IL............. 5.3 126.3 -2.3 47 928 3.5 114
Will, IL................. 14.2 188.4 -4.3 184 837 1.2 268
Winnebago, IL............ 6.9 123.8 -7.6 322 797 2.7 173
Allen, IN................ 9.1 170.7 -4.7 215 774 3.3 126
Elkhart, IN.............. 4.9 96.4 -4.8 221 744 8.6 4
Hamilton, IN............. 7.9 107.1 -5.1 241 873 2.2 214
Lake, IN................. 10.3 183.6 -5.3 248 798 -3.4 325
Marion, IN............... 24.0 547.0 -3.8 147 942 3.1 136
St. Joseph, IN........... 6.1 114.9 -5.5 257 799 5.1 35
Tippecanoe, IN........... 3.3 72.1 -6.8 310 800 3.8 96
Vanderburgh, IN.......... 4.8 104.7 -3.2 100 791 3.3 126
Johnson, IA.............. 3.5 74.5 -2.1 36 807 2.7 173
Linn, IA................. 6.3 123.6 -3.0 85 885 -1.1 313
Polk, IA................. 14.8 265.7 -3.1 88 933 3.1 136
Scott, IA................ 5.3 84.9 -4.6 209 763 1.6 247
Johnson, KS.............. 20.9 298.8 -5.0 231 982 3.4 119
Sedgwick, KS............. 12.5 241.3 -7.7 324 872 3.3 126
Shawnee, KS.............. 4.9 92.9 -2.9 76 798 5.4 30
Wyandotte, KS............ 3.2 79.0 -1.5 17 890 4.3 67
Fayette, KY.............. 9.4 172.9 -2.9 76 846 1.7 244
Jefferson, KY............ 22.0 409.9 -3.2 100 908 4.2 74
Caddo, LA................ 7.5 121.9 -2.7 64 790 1.4 257
Calcasieu, LA............ 5.0 83.1 -5.4 253 783 -5.1 326
East Baton Rouge, LA..... 14.7 259.1 -3.1 88 897 2.6 180
Jefferson, LA............ 14.2 194.5 -3.0 85 896 2.6 180
Lafayette, LA............ 9.1 129.6 -5.5 257 887 -2.6 324
Orleans, LA.............. 10.9 169.4 -1.5 17 1,007 0.5 293
Cumberland, ME........... 12.3 168.0 -3.2 100 863 4.7 46
Anne Arundel, MD......... 14.3 226.4 -3.1 88 1,019 5.6 22
Baltimore, MD............ 21.2 364.8 -3.4 117 1,004 4.0 83
Frederick, MD............ 5.9 91.4 -2.9 76 933 4.7 46
Harford, MD.............. 5.6 81.6 -1.1 11 896 6.2 12
Howard, MD............... 8.7 143.0 -2.9 76 1,131 4.0 83
Montgomery, MD........... 32.5 447.4 -2.1 36 1,294 6.2 12
Prince Georges, MD....... 15.5 304.2 -3.4 117 1,032 3.8 96
Baltimore City, MD....... 13.7 326.1 -3.4 117 1,113 1.2 268
Barnstable, MA........... 9.0 82.7 -1.9 27 834 2.7 173
Bristol, MA.............. 15.6 207.4 -4.1 170 866 2.2 214
Essex, MA................ 21.0 293.0 -2.0 32 1,013 3.6 110
Hampden, MA.............. 14.8 192.3 -3.6 134 893 2.8 167
Middlesex, MA............ 47.9 803.0 -2.8 70 1,344 3.5 114
Norfolk, MA.............. 23.6 312.9 -3.4 117 1,151 0.5 293
Plymouth, MA............. 13.6 171.3 -2.5 56 902 1.0 276
Suffolk, MA.............. 22.2 574.8 -3.5 128 1,565 -0.3 309
Worcester, MA............ 20.8 309.8 -3.0 85 947 1.6 247
Genesee, MI.............. 7.6 127.0 -5.5 257 826 3.0 141
Ingham, MI............... 6.6 151.1 -4.3 184 913 3.0 141
Kalamazoo, MI............ 5.5 108.0 -4.6 209 842 -1.3 317
Kent, MI................. 14.0 305.9 -5.0 231 855 2.4 197
Macomb, MI............... 17.2 270.8 -7.3 319 976 1.0 276
Oakland, MI.............. 37.9 607.1 -8.1 327 1,093 -0.5 311
Ottawa, MI............... 5.6 98.0 -5.9 282 787 -0.4 310
Saginaw, MI.............. 4.2 79.1 (7) - 782 (7) -
Washtenaw, MI............ 8.0 184.2 -1.8 24 981 1.0 276
Wayne, MI................ 31.1 662.6 -7.1 317 1,036 0.5 293
Anoka, MN................ 7.4 105.8 -6.6 305 858 2.3 208
Dakota, MN............... 10.0 168.8 -2.6 62 920 2.6 180
Hennepin, MN............. 40.7 802.6 -4.3 184 1,152 0.7 288
Olmsted, MN.............. 3.4 87.3 -2.8 70 994 1.9 230
Ramsey, MN............... 14.5 316.0 -4.3 184 1,040 6.0 15
St. Louis, MN............ 5.7 92.2 -4.2 178 755 -0.7 312
Stearns, MN.............. 4.3 78.1 -3.3 107 747 5.8 17
Harrison, MS............. 4.6 83.4 -2.1 36 718 2.3 208
Hinds, MS................ 6.3 125.0 -2.4 50 832 3.4 119
Boone, MO................ 4.5 81.2 -1.7 22 719 4.2 74
Clay, MO................. 5.0 84.8 -5.2 245 856 4.3 67
Greene, MO............... 8.0 149.2 -4.2 178 711 3.9 87
Jackson, MO.............. 18.5 351.2 -4.3 184 958 3.2 129
St. Charles, MO.......... 8.2 117.8 -4.1 170 739 0.8 285
St. Louis, MO............ 32.1 571.0 -4.7 215 1,006 1.5 252
St. Louis City, MO....... 8.6 215.2 (7) - 996 -33.9 329
Yellowstone, MT.......... 5.9 75.7 -3.4 117 768 3.9 87
Douglas, NE.............. 15.9 312.1 -3.1 88 874 3.9 87
Lancaster, NE............ 8.2 153.2 -3.9 156 750 3.2 129
Clark, NV................ 49.4 809.7 -7.0 312 872 1.9 230
Washoe, NV............... 14.3 187.4 -7.0 312 868 0.1 304
Hillsborough, NH......... 12.1 188.3 -3.9 156 1,065 0.2 303
Rockingham, NH........... 10.8 131.9 -3.2 100 930 2.6 180
Atlantic, NJ............. 7.0 133.3 -4.5 201 832 1.2 268
Bergen, NJ............... 34.5 432.8 -3.8 147 1,205 1.7 244
Burlington, NJ........... 11.4 194.6 -2.7 64 1,011 3.8 96
Camden, NJ............... 13.0 198.8 -2.9 76 1,010 0.9 280
Essex, NJ................ 21.5 348.2 -2.7 64 1,211 2.7 173
Gloucester, NJ........... 6.4 100.4 -4.3 184 865 1.3 262
Hudson, NJ............... 14.1 232.4 -2.8 70 1,241 2.4 197
Mercer, NJ............... 11.2 226.1 -2.2 41 1,224 -2.2 322
Middlesex, NJ............ 22.1 385.0 -3.4 117 1,160 1.4 257
Monmouth, NJ............. 20.8 246.3 -3.3 107 1,032 1.3 262
Morris, NJ............... 18.1 274.3 -3.5 128 1,429 4.7 46
Ocean, NJ................ 12.4 144.3 -1.3 13 816 2.6 180
Passaic, NJ.............. 12.5 171.3 -3.1 88 997 2.3 208
Somerset, NJ............. 10.3 168.8 -3.3 107 1,413 -6.2 328
Union, NJ................ 14.9 221.3 -3.3 107 1,226 (7) -
Bernalillo, NM........... 17.5 317.3 -3.7 140 850 4.4 61
Albany, NY............... 9.9 223.3 -2.6 62 963 1.9 230
Bronx, NY................ 16.4 232.7 0.2 2 919 3.5 114
Broome, NY............... 4.5 92.7 -3.3 107 753 3.7 103
Dutchess, NY............. 8.2 113.1 -2.8 70 942 4.7 46
Erie, NY................. 23.5 453.4 -2.5 56 817 3.0 141
Kings, NY................ 48.3 488.8 0.2 2 830 1.2 268
Monroe, NY............... 17.9 371.8 -2.9 76 887 3.0 141
Nassau, NY............... 52.4 595.3 -2.2 41 1,101 4.3 67
New York, NY............. 118.1 2,294.4 -3.9 156 1,878 1.1 273
Oneida, NY............... 5.3 109.3 -2.3 47 745 3.2 129
Onondaga, NY............. 12.7 246.1 -3.1 88 881 3.8 96
Orange, NY............... 9.9 131.4 -1.3 13 799 2.8 167
Queens, NY............... 44.2 499.4 -2.1 36 935 1.3 262
Richmond, NY............. 8.8 94.6 -1.1 11 850 2.9 153
Rockland, NY............. 9.8 114.3 -2.8 70 982 -2.1 321
Saratoga, NY............. 5.4 74.6 -2.5 56 792 3.9 87
Suffolk, NY.............. 50.0 608.5 -3.1 88 1,044 0.3 301
Westchester, NY.......... 36.0 406.5 -4.0 162 1,288 4.4 61
Buncombe, NC............. 7.8 110.7 -4.0 162 747 2.9 153
Catawba, NC.............. 4.4 77.3 -7.5 321 725 4.2 74
Cumberland, NC........... 6.2 119.6 -1.4 15 749 5.5 27
Durham, NC............... 7.1 177.3 -4.3 184 1,239 9.5 3
Forsyth, NC.............. 9.0 176.2 -4.6 209 849 2.9 153
Guilford, NC............. 14.3 260.1 -5.4 253 823 3.0 141
Mecklenburg, NC.......... 32.3 534.2 -5.7 272 1,042 2.5 193
New Hanover, NC.......... 7.3 95.3 -5.8 276 798 5.6 22
Wake, NC................. 28.5 430.7 -4.1 170 929 1.5 252
Cass, ND................. 5.9 99.3 -1.4 15 795 2.1 222
Butler, OH............... 7.3 138.2 -5.0 231 819 4.6 55
Cuyahoga, OH............. 36.7 689.8 -4.7 215 939 1.2 268
Franklin, OH............. 29.4 651.3 -3.7 140 918 4.3 67
Hamilton, OH............. 23.5 488.6 -4.6 209 1,007 2.4 197
Lake, OH................. 6.6 92.1 -6.7 309 777 2.8 167
Lorain, OH............... 6.2 91.8 -4.1 170 742 0.0 306
Lucas, OH................ 10.5 198.5 -5.5 257 830 7.0 10
Mahoning, OH............. 6.2 97.7 -2.7 64 683 1.9 230
Montgomery, OH........... 12.5 242.1 -5.5 257 846 2.9 153
Stark, OH................ 8.9 149.2 -5.8 276 713 1.4 257
Summit, OH............... 14.7 254.2 -6.3 297 842 2.1 222
Trumbull, OH............. 4.6 68.9 -8.6 328 739 -1.3 317
Warren, OH............... 4.2 71.7 -3.5 128 802 5.4 30
Oklahoma, OK............. 24.1 408.0 -4.4 196 870 1.9 230
Tulsa, OK................ 19.8 331.0 -5.6 263 845 0.8 285
Clackamas, OR............ 12.6 138.5 -5.3 248 842 2.4 197
Jackson, OR.............. 6.5 76.2 -5.8 276 688 3.1 136
Lane, OR................. 10.9 135.4 -5.9 282 729 2.5 193
Marion, OR............... 9.3 130.4 -3.4 117 727 2.3 208
Multnomah, OR............ 28.1 421.9 -4.9 225 953 1.9 230
Washington, OR........... 16.0 230.9 -5.0 231 1,031 4.5 58
Allegheny, PA............ 35.1 668.8 -2.4 50 1,003 2.9 153
Berks, PA................ 9.0 161.5 -3.8 147 849 3.8 96
Bucks, PA................ 19.7 249.3 -4.2 178 930 2.9 153
Butler, PA............... 4.8 79.5 -1.6 20 831 3.0 141
Chester, PA.............. 15.0 236.1 -3.3 107 1,233 5.4 30
Cumberland, PA........... 6.0 120.8 -3.4 117 869 6.0 15
Dauphin, PA.............. 7.4 177.8 -2.1 36 924 4.3 67
Delaware, PA............. 13.5 205.2 -3.2 100 994 4.3 67
Erie, PA................. 7.6 120.9 -4.3 184 735 0.7 288
Lackawanna, PA........... 5.9 98.7 -2.9 76 736 3.1 136
Lancaster, PA............ 12.5 217.7 -4.2 178 789 2.2 214
Lehigh, PA............... 8.7 170.4 -3.7 140 921 1.5 252
Luzerne, PA.............. 7.8 137.5 -3.4 117 735 5.6 22
Montgomery, PA........... 27.4 469.0 -3.5 128 1,219 5.5 27
Northampton, PA.......... 6.5 97.7 -1.5 17 823 2.1 222
Philadelphia, PA......... 31.4 624.5 -2.4 50 1,145 4.7 46
Washington, PA........... 5.4 77.5 -3.7 140 847 4.2 74
Westmoreland, PA......... 9.4 130.2 -3.9 156 751 3.0 141
York, PA................. 9.0 168.8 -4.7 215 810 2.9 153
Kent, RI................. 5.5 73.7 -5.1 241 828 5.6 22
Providence, RI........... 17.7 267.0 -4.0 162 951 1.9 230
Charleston, SC........... 11.6 198.1 -5.6 263 821 5.1 35
Greenville, SC........... 12.4 224.0 -4.5 201 820 2.9 153
Horry, SC................ 7.9 100.1 -5.6 263 584 1.4 257
Lexington, SC............ 5.6 93.0 -5.0 231 710 4.1 82
Richland, SC............. 9.2 205.1 -4.3 184 826 4.7 46
Spartanburg, SC.......... 6.0 111.9 -5.4 253 803 3.7 103
Minnehaha, SD............ 6.5 113.2 -2.5 56 777 5.0 39
Davidson, TN............. 18.3 418.3 -4.0 162 996 2.2 214
Hamilton, TN............. 8.5 177.4 -6.1 289 821 0.6 292
Knox, TN................. 10.9 217.2 -4.4 196 835 4.5 58
Rutherford, TN........... 4.3 93.5 -4.6 209 846 0.4 299
Shelby, TN............... 19.4 471.5 -5.0 231 971 3.9 87
Williamson, TN........... 6.0 85.7 -2.9 76 1,012 3.0 141
Bell, TX................. 4.6 103.5 -0.7 7 741 5.1 35
Bexar, TX................ 33.0 719.1 -1.9 27 843 4.7 46
Brazoria, TX............. 4.7 84.0 -5.0 231 845 -2.3 323
Brazos, TX............... 3.9 87.2 (7) - 695 (7) -
Cameron, TX.............. 6.4 123.7 -0.8 8 598 2.2 214
Collin, TX............... 17.5 282.4 (7) - 1,108 5.5 27
Dallas, TX............... 67.8 1,409.9 -4.3 184 1,129 0.5 293
Denton, TX............... 10.7 167.9 -1.8 24 827 2.2 214
El Paso, TX.............. 13.5 269.5 -1.7 22 684 6.4 11
Fort Bend, TX............ 8.8 128.9 -3.4 117 954 -2.0 320
Galveston, TX............ 5.2 93.1 -0.2 5 877 5.8 17
Gregg, TX................ 4.1 71.5 -5.6 263 821 -1.2 316
Harris, TX............... 98.7 1,990.2 -4.3 184 1,195 0.7 288
Hidalgo, TX.............. 10.7 220.4 -1.0 10 598 4.2 74
Jefferson, TX............ 5.9 119.8 -6.1 289 924 -1.5 319
Lubbock, TX.............. 6.9 123.9 -2.2 41 718 2.6 180
McLennan, TX............. 4.9 101.4 -2.0 32 772 7.4 9
Montgomery, TX........... 8.4 127.1 -2.0 32 879 0.5 293
Nueces, TX............... 8.0 151.2 (7) - 794 (7) -
Potter, TX............... 3.9 74.9 -2.0 32 798 2.3 208
Smith, TX................ 5.3 92.2 -3.6 134 811 0.4 299
Tarrant, TX.............. 37.3 748.1 -3.1 88 947 3.2 129
Travis, TX............... 29.5 558.5 -3.3 107 1,036 2.6 180
Webb, TX................. 4.7 86.0 -3.5 128 619 3.0 141
Williamson, TX........... 7.4 120.2 -1.9 27 906 1.1 273
Davis, UT................ 7.2 99.2 -2.8 70 764 3.0 141
Salt Lake, UT............ 37.5 562.1 -4.1 170 888 4.7 46
Utah, UT................. 13.0 164.6 -4.4 196 741 1.8 241
Weber, UT................ 5.7 88.5 -5.0 231 705 3.7 103
Chittenden, VT........... 6.0 93.2 -2.3 47 937 4.6 55
Arlington, VA............ 8.0 160.9 0.5 1 1,594 5.8 17
Chesterfield, VA......... 7.6 115.0 -4.3 184 852 3.0 141
Fairfax, VA.............. 34.3 574.6 -1.9 27 1,489 5.2 33
Henrico, VA.............. 9.7 169.8 -5.8 276 945 2.9 153
Loudoun, VA.............. 9.2 131.1 -1.9 27 1,141 4.8 43
Prince William, VA....... 7.4 103.5 -0.8 8 848 3.9 87
Alexandria City, VA...... 6.1 98.6 -2.7 64 1,376 4.8 43
Chesapeake City, VA...... 5.7 94.9 -3.8 147 761 6.1 14
Newport News City, VA.... 3.9 96.4 -3.5 128 873 2.6 180
Norfolk City, VA......... 5.8 138.1 -3.1 88 946 4.5 58
Richmond City, VA........ 7.3 150.2 -3.2 100 1,021 0.9 280
Virginia Beach City, VA.. 11.5 164.4 -3.3 107 756 4.0 83
Clark, WA................ 13.3 126.8 -2.2 41 842 3.2 129
King, WA................. 82.1 1,119.1 -4.7 215 1,172 3.6 110
Kitsap, WA............... 6.8 81.6 -1.8 24 858 4.6 55
Pierce, WA............... 21.9 261.4 -3.4 117 846 3.9 87
Snohomish, WA............ 18.9 238.1 -5.2 245 969 4.4 61
Spokane, WA.............. 16.2 198.2 -4.1 170 771 4.8 43
Thurston, WA............. 7.4 97.3 -2.2 41 830 2.6 180
Whatcom, WA.............. 7.1 77.5 -3.6 134 734 3.7 103
Yakima, WA............... 8.9 90.9 -2.5 56 640 2.4 197
Kanawha, WV.............. 6.0 105.7 -3.3 107 819 2.4 197
Brown, WI................ 6.7 143.6 -3.6 134 851 3.8 96
Dane, WI................. 13.9 295.6 -3.1 88 897 2.4 197
Milwaukee, WI............ 21.2 470.3 -4.9 225 948 2.9 153
Outagamie, WI............ 5.0 100.3 -4.9 225 792 1.1 273
Racine, WI............... 4.1 70.5 -6.1 289 867 -1.1 313
Waukesha, WI............. 12.9 218.1 -6.4 300 919 0.1 304
Winnebago, WI............ 3.7 87.9 -2.5 56 870 1.5 252
San Juan, PR............. 11.8 276.8 -4.6 (8) 653 4.8 (8)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 334 U.S. counties comprise 71.4 percent of the total covered workers
in the U.S.
(2) Data are preliminary.
(3) Includes areas not officially designated as counties. See Technical Note.
(4) Average weekly wages were calculated using unrounded data.
(5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(6) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(7) Data do not meet BLS or State agency disclosure standards.
(8) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties,
fourth quarter 2009(2)
Employment Average weekly
wage(3)
Establishments,
fourth quarter
County by NAICS supersector 2009 Percent Percent
(thousands) December change, Average change,
2009 December weekly fourth
(thousands) 2008-09(4) wage quarter
2008-09(4)
United States(5)............................. 9,085.0 128,334.9 -4.1 $942 2.5
Private industry........................... 8,790.5 106,313.0 -4.9 942 2.4
Natural resources and mining............. 126.9 1,649.6 -8.5 985 -1.1
Construction............................. 827.3 5,558.7 -16.2 1,053 0.1
Manufacturing............................ 349.9 11,484.8 -10.9 1,148 4.9
Trade, transportation, and utilities..... 1,886.7 25,057.0 -4.8 783 2.2
Information.............................. 145.7 2,766.2 -6.3 1,448 6.4
Financial activities..................... 834.7 7,498.6 -4.6 1,422 2.3
Professional and business services....... 1,534.3 16,512.5 -4.9 1,237 2.9
Education and health services............ 876.0 18,597.7 1.6 911 4.5
Leisure and hospitality.................. 742.6 12,621.7 -2.6 399 2.3
Other services........................... 1,261.9 4,343.0 -2.4 589 1.4
Government................................. 294.5 22,022.0 -0.4 942 3.1
Los Angeles, CA.............................. 434.0 3,926.0 -5.3 1,099 2.0
Private industry........................... 430.1 3,342.6 -5.7 1,093 2.4
Natural resources and mining............. 0.5 9.3 -10.6 1,473 16.6
Construction............................. 13.6 107.1 -21.2 1,154 1.3
Manufacturing............................ 13.9 375.8 -10.5 1,169 6.3
Trade, transportation, and utilities..... 52.4 752.7 -6.1 858 3.5
Information.............................. 8.8 199.0 -4.4 2,045 7.2
Financial activities..................... 23.2 217.3 -6.1 1,487 1.5
Professional and business services....... 42.5 526.0 -8.1 1,339 1.7
Education and health services............ 28.5 504.6 0.6 1,034 5.6
Leisure and hospitality.................. 27.4 380.2 -4.5 908 -3.4
Other services........................... 204.6 253.7 -1.4 449 -1.3
Government................................. 3.9 583.4 -2.4 1,136 -0.4
Cook, IL..................................... 142.6 2,369.9 -4.5 1,142 2.1
Private industry........................... 141.2 2,062.3 -5.0 1,141 1.2
Natural resources and mining............. 0.1 0.9 -11.2 1,071 -0.6
Construction............................. 12.2 69.1 -16.0 1,407 -4.6
Manufacturing............................ 6.8 196.5 -10.1 1,158 3.7
Trade, transportation, and utilities..... 27.5 444.4 -5.7 843 0.8
Information.............................. 2.6 52.1 -5.9 1,622 9.1
Financial activities..................... 15.4 190.9 -6.6 2,063 2.0
Professional and business services....... 29.5 396.2 -6.7 1,542 0.7
Education and health services............ 14.5 392.6 1.6 976 5.1
Leisure and hospitality.................. 12.2 220.9 -2.4 454 2.0
Other services........................... 15.1 93.9 -2.9 792 1.4
Government................................. 1.4 307.6 -1.0 1,148 8.4
New York, NY................................. 118.1 2,294.4 -3.9 1,878 1.1
Private industry........................... 117.9 1,845.7 -4.7 2,072 1.5
Natural resources and mining............. 0.0 0.1 -8.9 1,795 12.0
Construction............................. 2.2 31.0 -15.3 2,062 6.1
Manufacturing............................ 2.7 27.3 -17.4 1,582 5.2
Trade, transportation, and utilities..... 21.0 241.2 -5.5 1,316 1.6
Information.............................. 4.4 124.9 -7.4 2,144 4.1
Financial activities..................... 18.7 345.1 -7.2 4,264 4.6
Professional and business services....... 24.6 459.7 -6.3 2,148 -1.1
Education and health services............ 8.8 298.9 1.3 1,180 4.1
Leisure and hospitality.................. 11.9 223.7 -1.2 927 3.8
Other services........................... 18.1 88.2 -2.0 1,112 1.0
Government................................. 0.3 448.7 -0.8 1,087 2.3
Harris, TX................................... 98.7 1,990.2 -4.3 1,195 0.7
Private industry........................... 98.2 1,726.5 -5.3 1,225 0.8
Natural resources and mining............. 1.5 80.3 -5.9 3,130 9.4
Construction............................. 6.6 134.7 -14.5 1,229 1.1
Manufacturing............................ 4.6 166.9 -12.3 1,494 1.4
Trade, transportation, and utilities..... 22.4 421.5 -4.7 1,027 -0.5
Information.............................. 1.4 30.2 -4.8 1,381 -0.4
Financial activities..................... 10.6 114.2 -4.0 1,456 -3.4
Professional and business services....... 19.8 311.4 -7.3 1,494 2.5
Education and health services............ 10.7 232.9 4.0 990 3.3
Leisure and hospitality.................. 7.9 175.0 -0.8 414 2.7
Other services........................... 12.4 58.7 -2.6 660 -2.4
Government................................. 0.5 263.7 2.4 997 1.0
Maricopa, AZ................................. 98.7 1,626.8 -6.5 923 3.4
Private industry........................... 98.0 1,407.7 -6.9 920 2.8
Natural resources and mining............. 0.5 7.9 -6.4 857 -16.6
Construction............................. 9.8 82.8 -28.5 998 1.1
Manufacturing............................ 3.3 106.7 -11.5 1,272 4.4
Trade, transportation, and utilities..... 22.4 345.4 -5.5 824 3.3
Information.............................. 1.5 27.5 -6.8 1,227 11.0
Financial activities..................... 12.1 134.3 -4.5 1,094 2.5
Professional and business services....... 22.3 265.2 -7.9 1,007 1.6
Education and health services............ 10.3 224.1 3.2 1,037 3.9
Leisure and hospitality.................. 7.1 166.3 -5.9 440 4.3
Other services........................... 7.1 46.6 -4.6 655 6.0
Government................................. 0.7 219.1 -4.0 940 6.6
Dallas, TX................................... 67.8 1,409.9 -4.3 1,129 0.5
Private industry........................... 67.3 1,240.9 -4.9 1,144 0.3
Natural resources and mining............. 0.6 8.3 -0.5 3,746 -22.4
Construction............................. 4.2 67.6 -15.9 1,110 3.4
Manufacturing............................ 3.0 116.5 -11.2 1,279 (6)
Trade, transportation, and utilities..... 14.9 288.7 -5.1 997 0.7
Information.............................. 1.6 45.5 -5.0 1,564 3.2
Financial activities..................... 8.6 137.0 (6) 1,427 (6)
Professional and business services....... 14.8 251.3 -7.4 1,377 0.0
Education and health services............ 6.9 162.2 6.1 1,067 1.0
Leisure and hospitality.................. 5.4 124.9 -3.0 514 4.5
Other services........................... 6.9 38.1 -2.2 672 -0.3
Government................................. 0.5 169.0 -0.1 1,018 3.2
Orange, CA................................... 102.8 1,361.4 -6.2 1,065 2.0
Private industry........................... 101.5 1,215.9 -6.5 1,067 2.2
Natural resources and mining............. 0.2 3.3 -16.9 637 -5.5
Construction............................. 6.7 67.8 -20.0 1,199 -2.1
Manufacturing............................ 5.1 149.4 -11.1 1,299 6.1
Trade, transportation, and utilities..... 16.6 253.8 -6.7 971 3.3
Information.............................. 1.3 26.0 -10.0 1,546 7.3
Financial activities..................... 10.2 104.8 (6) 1,643 3.4
Professional and business services....... 19.0 238.5 (6) 1,279 0.6
Education and health services............ 10.2 152.1 0.0 1,014 5.7
Leisure and hospitality.................. 7.1 166.5 -3.1 417 3.5
Other services........................... 20.0 47.8 -2.7 556 -0.7
Government................................. 1.4 145.5 -3.1 1,048 0.4
San Diego, CA................................ 99.4 1,245.3 -4.9 1,019 3.7
Private industry........................... 98.1 1,021.4 -5.8 1,005 4.4
Natural resources and mining............. 0.7 8.6 -7.6 613 4.8
Construction............................. 6.7 57.0 -19.2 1,182 3.6
Manufacturing............................ 3.1 92.0 -9.7 1,411 7.5
Trade, transportation, and utilities..... 13.9 205.9 -5.6 785 (6)
Information.............................. 1.2 36.3 -6.1 2,156 9.8
Financial activities..................... 9.0 69.6 -5.1 1,185 0.5
Professional and business services....... 16.3 197.0 -6.3 1,320 4.8
Education and health services............ 8.3 144.6 2.5 990 4.3
Leisure and hospitality.................. 7.0 149.2 -6.3 442 3.3
Other services........................... 27.7 56.8 -3.6 512 7.6
Government................................. 1.3 224.0 -0.9 1,082 0.0
King, WA..................................... 82.1 1,119.1 -4.7 1,172 3.6
Private industry........................... 81.6 962.2 -5.4 1,180 3.4
Natural resources and mining............. 0.4 2.7 -7.9 1,321 -16.3
Construction............................. 6.6 48.8 -22.8 1,255 5.0
Manufacturing............................ 2.4 98.5 -9.4 1,504 3.7
Trade, transportation, and utilities..... 15.2 209.1 -5.5 996 4.0
Information.............................. 1.8 78.4 -4.3 2,016 2.1
Financial activities..................... 6.9 66.2 -7.9 1,515 6.4
Professional and business services....... 14.5 171.9 -7.5 1,449 5.3
Education and health services............ 6.9 131.6 1.8 968 8.0
Leisure and hospitality.................. 6.4 105.8 -2.7 469 4.5
Other services........................... 20.5 49.2 12.6 598 -5.7
Government................................. 0.5 157.0 0.0 1,122 4.9
Miami-Dade, FL............................... 85.0 959.7 -4.5 949 2.9
Private industry........................... 84.6 811.8 -4.7 919 1.7
Natural resources and mining............. 0.5 9.5 -3.2 483 7.3
Construction............................. 5.6 32.9 -21.1 980 0.8
Manufacturing............................ 2.6 35.5 -14.1 914 10.1
Trade, transportation, and utilities..... 23.3 242.0 -4.4 834 2.8
Information.............................. 1.5 17.4 -8.6 1,340 6.3
Financial activities..................... 9.5 62.2 -6.2 1,397 0.1
Professional and business services....... 17.7 123.4 -7.0 1,215 -1.0
Education and health services............ 9.6 150.2 3.0 915 1.7
Leisure and hospitality.................. 6.1 103.5 -1.9 538 6.5
Other services........................... 7.5 34.7 -4.9 576 -0.9
Government................................. 0.4 147.8 -3.2 1,112 9.3
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
(2) Data are preliminary.
(3) Average weekly wages were calculated using unrounded data.
(4) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(5) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(6) Data do not meet BLS or State agency disclosure standards.
Table 3. Covered(1) establishments, employment, and wages in the largest county by
state, fourth quarter 2009(2)
Employment Average weekly
wage(4)
Establishments,
fourth quarter
County(3) 2009 Percent Percent
(thousands) December change, Average change,
2009 December weekly fourth
(thousands) 2008-09(5) wage quarter
2008-09(5)
United States(6)......... 9,085.0 128,334.9 -4.1 $942 2.5
Jefferson, AL............ 18.1 336.1 -5.6 946 2.5
Anchorage Borough, AK.... 8.2 147.6 -0.3 1,005 3.5
Maricopa, AZ............. 98.7 1,626.8 -6.5 923 3.4
Pulaski, AR.............. 15.1 244.2 -2.7 863 1.6
Los Angeles, CA.......... 434.0 3,926.0 -5.3 1,099 2.0
Denver, CO............... 25.0 420.2 -4.7 1,154 3.4
Hartford, CT............. 25.4 486.0 -3.8 1,153 3.6
New Castle, DE........... 17.8 264.6 -5.9 1,070 1.9
Washington, DC........... 34.8 686.7 -0.1 1,614 2.7
Miami-Dade, FL........... 85.0 959.7 -4.5 949 2.9
Fulton, GA............... 39.3 697.4 -5.0 1,207 1.9
Honolulu, HI............. 25.0 435.3 -3.2 875 2.9
Ada, ID.................. 14.5 193.7 -4.9 824 1.5
Cook, IL................. 142.6 2,369.9 -4.5 1,142 2.1
Marion, IN............... 24.0 547.0 -3.8 942 3.1
Polk, IA................. 14.8 265.7 -3.1 933 3.1
Johnson, KS.............. 20.9 298.8 -5.0 982 3.4
Jefferson, KY............ 22.0 409.9 -3.2 908 4.2
East Baton Rouge, LA..... 14.7 259.1 -3.1 897 2.6
Cumberland, ME........... 12.3 168.0 -3.2 863 4.7
Montgomery, MD........... 32.5 447.4 -2.1 1,294 6.2
Middlesex, MA............ 47.9 803.0 -2.8 1,344 3.5
Wayne, MI................ 31.1 662.6 -7.1 1,036 0.5
Hennepin, MN............. 40.7 802.6 -4.3 1,152 0.7
Hinds, MS................ 6.3 125.0 -2.4 832 3.4
St. Louis, MO............ 32.1 571.0 -4.7 1,006 1.5
Yellowstone, MT.......... 5.9 75.7 -3.4 768 3.9
Douglas, NE.............. 15.9 312.1 -3.1 874 3.9
Clark, NV................ 49.4 809.7 -7.0 872 1.9
Hillsborough, NH......... 12.1 188.3 -3.9 1,065 0.2
Bergen, NJ............... 34.5 432.8 -3.8 1,205 1.7
Bernalillo, NM........... 17.5 317.3 -3.7 850 4.4
New York, NY............. 118.1 2,294.4 -3.9 1,878 1.1
Mecklenburg, NC.......... 32.3 534.2 -5.7 1,042 2.5
Cass, ND................. 5.9 99.3 -1.4 795 2.1
Cuyahoga, OH............. 36.7 689.8 -4.7 939 1.2
Oklahoma, OK............. 24.1 408.0 -4.4 870 1.9
Multnomah, OR............ 28.1 421.9 -4.9 953 1.9
Allegheny, PA............ 35.1 668.8 -2.4 1,003 2.9
Providence, RI........... 17.7 267.0 -4.0 951 1.9
Greenville, SC........... 12.4 224.0 -4.5 820 2.9
Minnehaha, SD............ 6.5 113.2 -2.5 777 5.0
Shelby, TN............... 19.4 471.5 -5.0 971 3.9
Harris, TX............... 98.7 1,990.2 -4.3 1,195 0.7
Salt Lake, UT............ 37.5 562.1 -4.1 888 4.7
Chittenden, VT........... 6.0 93.2 -2.3 937 4.6
Fairfax, VA.............. 34.3 574.6 -1.9 1,489 5.2
King, WA................. 82.1 1,119.1 -4.7 1,172 3.6
Kanawha, WV.............. 6.0 105.7 -3.3 819 2.4
Milwaukee, WI............ 21.2 470.3 -4.9 948 2.9
Laramie, WY.............. 3.2 42.6 -3.2 778 3.5
San Juan, PR............. 11.8 276.8 -4.6 653 4.8
St. Thomas, VI........... 1.8 23.3 -2.7 696 3.7
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(2) Data are preliminary.
(3) Includes areas not officially designated as counties. See Technical Note.
(4) Average weekly wages were calculated using unrounded data.
(5) Percent changes were computed from quarterly employment and pay data adjusted
for noneconomic county reclassifications. See Technical Note.
(6) Totals for the United States do not include data for Puerto Rico or the Virgin
Islands.
Table 4. Covered(1) establishments, employment, and wages by state,
fourth quarter 2009(2)
Employment Average weekly
wage(3)
Establishments,
fourth quarter
State 2009 Percent Percent
(thousands) December change, Average change,
2009 December weekly fourth
(thousands) 2008-09 wage quarter
2008-09
United States(4)......... 9,085.0 128,334.9 -4.1 $942 2.5
Alabama.................. 117.5 1,819.9 -4.7 818 3.4
Alaska................... 21.4 302.4 -0.5 959 3.5
Arizona.................. 154.1 2,406.2 -6.0 876 3.3
Arkansas................. 86.1 1,136.2 -2.8 725 2.5
California............... 1,374.0 14,476.4 -5.3 1,074 3.1
Colorado................. 171.7 2,183.6 -4.9 965 3.5
Connecticut.............. 112.0 1,620.1 -4.0 1,192 2.3
Delaware................. 28.6 398.3 -5.0 960 2.1
District of Columbia..... 34.8 686.7 -0.1 1,614 2.7
Florida.................. 599.3 7,208.9 -5.0 855 3.6
Georgia.................. 271.6 3,773.5 -4.9 875 2.6
Hawaii................... 39.3 592.5 -3.7 843 2.7
Idaho.................... 55.8 604.3 -4.7 708 2.2
Illinois................. 376.4 5,529.4 -4.6 1,008 2.3
Indiana.................. 159.9 2,709.7 -4.3 781 2.2
Iowa..................... 94.6 1,436.2 -3.3 771 2.1
Kansas................... 88.1 1,309.8 -4.4 792 2.9
Kentucky................. 108.2 1,726.2 -3.1 781 3.4
Louisiana................ 127.0 1,842.8 -3.5 833 0.4
Maine.................... 50.2 579.0 -2.8 759 3.3
Maryland................. 162.4 2,462.9 -2.8 1,054 4.5
Massachusetts............ 215.5 3,142.5 -3.0 1,176 1.8
Michigan................. 252.2 3,767.7 -5.6 913 1.1
Minnesota................ 166.0 2,559.4 -3.8 928 2.3
Mississippi.............. 70.7 1,076.5 -3.7 697 2.7
Missouri................. 174.3 2,598.7 -3.8 816 -3.2
Montana.................. 42.5 419.4 -3.3 695 2.5
Nebraska................. 60.5 896.6 -2.9 756 3.6
Nevada................... 74.9 1,123.2 -6.9 875 1.4
New Hampshire............ 48.9 605.8 -3.2 958 2.4
New Jersey............... 270.8 3,806.6 -2.9 1,143 1.6
New Mexico............... 54.1 787.0 -4.2 794 3.3
New York................. 586.4 8,445.4 -2.6 1,190 1.7
North Carolina........... 251.3 3,802.2 -5.0 818 3.2
North Dakota............. 26.0 353.6 -0.2 752 3.7
Ohio..................... 288.1 4,911.8 -4.9 840 2.9
Oklahoma................. 101.9 1,486.4 -4.8 763 0.9
Oregon................... 130.6 1,593.3 -4.8 829 2.5
Pennsylvania............. 342.0 5,474.5 -3.1 931 3.8
Rhode Island............. 35.3 448.1 -3.5 912 2.9
South Carolina........... 112.7 1,748.6 -4.9 763 4.4
South Dakota............. 31.0 386.0 -2.4 688 3.8
Tennessee................ 140.5 2,572.3 -4.5 849 2.9
Texas.................... 567.1 10,146.9 -3.5 944 1.2
Utah..................... 85.7 1,158.1 -4.5 796 3.2
Vermont.................. 24.6 296.4 -2.7 804 3.7
Virginia................. 231.7 3,551.6 -2.8 994 4.3
Washington............... 235.0 2,776.6 -3.7 952 3.6
West Virginia............ 48.5 693.6 -2.9 752 2.5
Wisconsin................ 158.2 2,634.2 -4.4 810 2.1
Wyoming.................. 25.1 266.9 -6.3 831 -2.2
Puerto Rico.............. 50.0 977.6 -5.2 552 4.5
Virgin Islands........... 3.5 43.9 -3.7 746 2.2
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(2) Data are preliminary.
(3) Average weekly wages were calculated using unrounded data.
(4) Totals for the United States do not include data for Puerto Rico or the
Virgin Islands.
United States Department of Labor
